http://www.ncbi.nlm.nih.gov/pubmed/23161674
Analysis of ~1900 RNA containing structures v ~50 non-redundant ones in http://papers.gersteinlab.org/papers/nucprot
This paper has: (1) Large-scale datasets compiled from literature and databases, (2) comprehensive gold standards for positive and negative samples, (3) a classifier algorithm (regularized Bayesian), and (4) further analysis beyond “functional prediction”, including an interaction network. It predicts a list of genes having some possible functions, and the authors have experimentally validated them.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2694471/
Genome Res. 2009 Jun;19(6):1093-106. Epub 2009 Feb 26.
Exploring the human genome with functional maps.
Huttenhower C, Haley EM, Hibbs MA, Dumeaux V, Barrett DR, Coller HA, Troyanskaya OG.
Large-scale genomic datasets can easily be transformed into various networks. The authors aimed to infer for each particular edge, whether or not it shows up in a particular tissue by training a model based on well curated tissue-specific expression as gold standards. The algorithm arrives at different tissue-specific networks from large-scale genomics datasets; without surprise, tissue-specific networks are more informative in predicting genes corresponding to diseases related to that particular tissue. For instance, a
testis-specific network performs better in predicting genes associated with male fertility phenotypes.
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002694 PLoS Comput Biol. 2012 Sep;8(9):e1002694.
doi: 10.1371/journal.pcbi.1002694. Epub 2012 Sep 27.
Tissue-specific functional networks for prioritizing phenotype and disease genes.
Guan Y, Gorenshteyn D, Burmeister M, Wong AK, Schimenti JC, Handel MA, Bult CJ, Hibbs MA, Troyanskaya O
This paper introduces a new method for detecting copy number variants in cancer genomes that addresses deficiencies of previous detection methods. The new method, dubbed HHCRF by the authors, adds the use of sequential correlations in selecting classification features for inferring copy numbers and identifying clinically relevant genes. This improvement results in higher accuracy on noisy data, and the identification of more clinically relevant genes, relative to previous methods. These results were obtained by testing HHCRF on both simulated array-CGH microarray data, and on actual breast cancer, uveal melanoma, and bladder tumor datasets.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2677736/
Bioinformatics. 2009 May 15;25(10):1307-13. Epub 2008 Dec 3. Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields.
Barutcuoglu Z, Airoldi EM, Dumeaux V, Schapire RE, Troyanskaya OG.
Hum Genet. 2012 Oct 10.
Ostrer H, Skorecki K.
Departments of Pathology and Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
http://www.ncbi.nlm.nih.gov/pubmed/23052947
Admixture analysis
http://www.ncbi.nlm.nih.gov/pubmed/22726445
Cell. 2012 Jun 22;149(7):1622-34.